Enterprise digital transformation as a double-edged sword for employees: an investigation based on the Job Demands-Resources model

Qiwei Zhou, Shuang Cheng, Feiqiang Fu (), Jingmin Zhang and Yingxu Jiang
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Qiwei Zhou: Ocean University of China
Shuang Cheng: Ocean University of China
Feiqiang Fu: Beijing University of Chemical Technology
Jingmin Zhang: Ocean University of China
Yingxu Jiang: Ocean University of China

Humanities and Social Sciences Communications, 2025, vol. 12, issue 1, 1-16

Abstract: Abstract The research on digital transformation, which has catalysed profound organisational changes, is booming. While existing studies primarily focus on either the positive or negative impact of digital transformation on employees, it remains unclear whether digital technologies exert differential effects based on employee characteristics. Given that heterogeneous employee responses may fundamentally shape the success of digital initiatives, this study constructs a double-edged sword effect model based on the Job Demands-Resources theory. It aims to examine the impact of digital technology affordance on employees’ adaptive performance and perceived status threat, specifically, the mediating role of job control and job demands, and the moderating role of employees’ goal orientation. Path analysis results based on a three-wave survey of 432 employees show that learning goal-oriented employees perceive digital technology affordance as enhancing job control, thereby improving adaptive performance. Conversely, performance goal-oriented employees interpret digital technology affordance as increasing job demands, which in turn amplifies the perceived status threat. This study enriches and extends the theoretical perspectives and application contexts of the Job Demands-Resources model, shifting the debate from whether digital technologies affect employees to understanding how and when these effects manifest differently. Furthermore, the findings offer practical guidance for organisations to adopt tailored managerial practices for different types of employees during digital transformation.

Date: 2025
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DOI: 10.1057/s41599-025-05985-4

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